Arcee-Spark
| Property | Value |
|---|---|
| Parameter Count | 7.62B |
| Model Type | Text Generation |
| Languages | English, German, Arabic |
| License | Apache 2.0 |
| Context Length | 128k tokens |
What is Arcee-Spark?
Arcee-Spark is a sophisticated 7.62B parameter language model that represents a significant advancement in efficient AI model design. Built upon the Qwen2 architecture, it underwent an intensive three-stage development process including fine-tuning on 1.8 million samples, strategic model merging, and Direct Preference Optimization (DPO). The model achieves remarkable performance metrics, notably scoring the highest on MT-Bench for models in its parameter class.
Implementation Details
The model employs BF16 tensor typing and leverages advanced training techniques to maximize its capabilities. Its architecture supports a substantial 128k token context window, enabling handling of extensive conversations and large text processing tasks.
- Advanced training methodology incorporating fine-tuning, model merging, and DPO
- Optimized for both performance and efficiency
- Multi-lingual support for English, German, and Arabic
- Available in multiple formats including GGUF and FP32 versions
Core Capabilities
- Achieves 56.21% accuracy on IFEval (0-Shot)
- Scores 37.14% on BBH (3-Shot)
- Demonstrates strong performance on MT-Bench with an average score of 8.47
- Excels in real-time applications and edge computing scenarios
- Suitable for business applications requiring low latency and high accuracy
Frequently Asked Questions
Q: What makes this model unique?
Arcee-Spark stands out for its exceptional performance-to-size ratio, achieving comparable results to GPT-3.5 while maintaining a smaller parameter count. Its sophisticated training process and optimization make it particularly efficient for real-world applications.
Q: What are the recommended use cases?
The model excels in real-time applications such as chatbots, customer service automation, edge computing deployments, and rapid prototyping scenarios. Its efficient architecture makes it ideal for organizations seeking to implement advanced AI capabilities without extensive computational resources.
